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Logo gboost 0.1.1

by nowozin - November 4, 2007, 07:52:21 CET [ Project Homepage BibTeX Download ] 5345 views, 1084 downloads, 0 subscriptions

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About: The gboost toolbox is a framework for classification of connected, undirected, labeled graphs.

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Initial Announcement on mloss.org.


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Logo WordNet Similarity 2.05

by tpederse - August 12, 2008, 16:42:50 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5336 views, 1326 downloads, 2 subscriptions

About: This is a Perl module that implements a variety of semantic similarity and relatedness measures based on information found in the lexical database WordNet. In particular, it supports the measures of [...]

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Initial Announcement on mloss.org.


Logo Spike train feature extraction by Bayesian binning 0.1

by dendres - September 24, 2008, 16:19:14 CET [ BibTeX BibTeX for corresponding Paper Download ] 5333 views, 1259 downloads, 0 comments, 1 subscription

About: *binsdfc* is a command line implementation of the algorithm described in [Endres,Oram,Schindelin,Foldiak:*Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms*, [...]

Changes:

Changed build system from automake to cmake. Moved download page to www.compsens.uni-tuebingen.de


Logo LibPG 126

by daa - December 3, 2007, 19:59:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5332 views, 1072 downloads, 0 subscriptions

About: The PG library is a high-performance reinforcement learning library. The name PG refers to policy-gradient methods, but this name is largely historical. The library also impliments value-based RL [...]

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Initial Announcement on mloss.org.


Logo Spider 1.71

by jaseweston - November 19, 2007, 15:51:59 CET [ Project Homepage BibTeX Download ] 5324 views, 1615 downloads, 0 subscriptions

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About: The spider is intended to be a complete object orientated environment for machine learning in Matlab. Aside from easy use of base learning algorithms, algorithms can be plugged together and can be [...]

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Initial Announcement on mloss.org.


Logo ILNumerics.Net 1.4.01

by haymo - October 14, 2008, 01:24:28 CET [ Project Homepage BibTeX Download ] 5286 views, 1273 downloads, 1 subscription

About: Intended for .NET developers wanting to implement algorithms directly in a common .NET language (recommended: C#). Support for n-dim generic arrays, LAPACK, FFT, cells, logicals, 2D&3D plotting [...]

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Initial Announcement on mloss.org.


Logo SeDuMi 1.21

by sonne - July 13, 2009, 10:22:00 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5268 views, 1077 downloads, 1 subscription

About: SeDuMi is a software package to solve optimization problems over symmetric cones. This includes linear, quadratic, second order conic and semidefinite optimization, and any combination of these.

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Initial Announcement on mloss.org.


Logo TiMBL 6.1

by antalvdb - January 11, 2008, 09:20:57 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5258 views, 1192 downloads, 0 comments, 0 subscriptions

About: The TiMBL software package is a fast, decision-tree-based implementation of k-nearest neighbor classification. The package includes the IB1, IB2, TRIBL, TRIBL2, and IGTree algorithms, and offers [...]

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Initial Announcement on mloss.org.


Logo Local Alignment Kernels 0.3.2

by hiroto - December 1, 2007, 00:10:23 CET [ Project Homepage BibTeX Download ] 5233 views, 1226 downloads, 0 subscriptions

About: Local alignment kernels measure the similarity between two sequences by summing up scores obtained from local alignments with gaps of the sequences.

Changes:

Initial Announcement on mloss.org.


About: This local and parallel computation toolbox is the Octave and Matlab implementation of several localized Gaussian process regression methods: the domain decomposition method (Park et al., 2011, DDM), partial independent conditional (Snelson and Ghahramani, 2007, PIC), localized probabilistic regression (Urtasun and Darrell, 2008, LPR), and bagging for Gaussian process regression (Chen and Ren, 2009, BGP). Most of the localized regression methods can be applied for general machine learning problems although DDM is only applicable for spatial datasets. In addition, the GPLP provides two parallel computation versions of the domain decomposition method. The easiness of being parallelized is one of the advantages of the localized regression, and the two parallel implementations will provide a good guidance about how to materialize this advantage as software.

Changes:

Initial Announcement on mloss.org.


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